{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "view-in-github"
   },
   "source": [
    "<a href=\"https://colab.research.google.com/github/CoreTheGreat/HBPU-Machine-Learning-Course/blob/main/ML_Chapter3_Classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "sM-ziKb94S9_",
    "outputId": "9a8bd59b-1ed4-47e3-e118-cee1849a610a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from sklearn import svm\n",
    "from sklearn import tree\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.multiclass import OneVsRestClassifier\n",
    "from sklearn.multiclass import OneVsOneClassifier\n",
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "import time\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "matplotlib.font_manager.fontManager.addfont('simhei.ttf') # Add the font\n",
    "matplotlib.rc('font', family='SimHei') # Set the font\n",
    "\n",
    "color_list = ['tab:blue', 'tab:orange', 'tab:green', 'tab:red', 'tab:purple', 'tab:brown', 'tab:pink', 'tab:gray', 'tab:olive', 'tab:cyan']\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "\n",
    "label_size = 18 # Label size\n",
    "ticklabel_size = 14 # Tick label size\n",
    "\n",
    "# Load the MNIST dataset to display\n",
    "imgDisp = torchvision.datasets.MNIST(root='data', train=False, download=True)\n",
    "img, label = imgDisp[0]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(7,7))\n",
    "ax.imshow(img, cmap='gray')\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size) # Set tick label size\n",
    "ax.set_title(f\"Label: {label}\", fontsize=label_size)\n",
    "# plt.savefig(f'exp_character{label}.png', dpi=300) # Make figure clearer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BWPSDyOq5qOO",
    "outputId": "9aed06f2-67a9-4a6f-f00c-d91554c3a260"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class 0: Trainset - 5923 (9.87%), Testset - 980 (9.80%)\n",
      "Class 1: Trainset - 6742 (11.24%), Testset - 1135 (11.35%)\n",
      "Class 2: Trainset - 5958 (9.93%), Testset - 1032 (10.32%)\n",
      "Class 3: Trainset - 6131 (10.22%), Testset - 1010 (10.10%)\n",
      "Class 4: Trainset - 5842 (9.74%), Testset - 982 (9.82%)\n",
      "Class 5: Trainset - 5421 (9.04%), Testset - 892 (8.92%)\n",
      "Class 6: Trainset - 5918 (9.86%), Testset - 958 (9.58%)\n",
      "Class 7: Trainset - 6265 (10.44%), Testset - 1028 (10.28%)\n",
      "Class 8: Trainset - 5851 (9.75%), Testset - 974 (9.74%)\n",
      "Class 9: Trainset - 5949 (9.92%), Testset - 1009 (10.09%)\n",
      "Input_size is 112\n"
     ]
    }
   ],
   "source": [
    "class ftrExtract(object):\n",
    "    def __call__(self, tensor):\n",
    "        tensor = tensor.squeeze()\n",
    "\n",
    "        mean_width = tensor.mean(axis=0)\n",
    "        mean_height = tensor.mean(axis=1)\n",
    "\n",
    "        std_width = tensor.std(axis=0)\n",
    "        std_height = tensor.std(axis=1)\n",
    "\n",
    "        ftrs = torch.cat([mean_width, mean_height, std_width, std_height])\n",
    "\n",
    "        return ftrs\n",
    "\n",
    "# Define a transform to normalize the data\n",
    "transform = transforms.Compose([transforms.ToTensor(), ftrExtract()])\n",
    "\n",
    "# Load the MNIST dataset\n",
    "trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)\n",
    "testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)\n",
    "\n",
    "# Count number of each class in trainset\n",
    "train_class_counts = {}\n",
    "for _, label in trainset:\n",
    "    if label not in train_class_counts:\n",
    "        train_class_counts[label] = 0\n",
    "    train_class_counts[label] += 1\n",
    "\n",
    "# Count number of each class in testset\n",
    "test_class_counts = {}\n",
    "for _, label in testset:\n",
    "    if label not in test_class_counts:\n",
    "        test_class_counts[label] = 0\n",
    "    test_class_counts[label] += 1\n",
    "\n",
    "# Print results\n",
    "for i in range(10):\n",
    "    cls_counts_train = train_class_counts.get(i, 0)\n",
    "    cls_ratio_train = cls_counts_train / len(trainset)\n",
    "    cls_counts_test = test_class_counts.get(i, 0)\n",
    "    cls_ratio_test = cls_counts_test / len(testset)\n",
    "\n",
    "    print(f\"Class {i}: Trainset - {cls_counts_train} ({cls_ratio_train:.2%}), Testset - {cls_counts_test} ({cls_ratio_test:.2%})\")\n",
    "\n",
    "batch_size = 42\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=0)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=0)\n",
    "\n",
    "# Get a batch of training data\n",
    "dataiter = iter(trainloader)\n",
    "data, labels = next(dataiter)\n",
    "\n",
    "input_size = data[0].numpy().shape[0]\n",
    "print(f'Input_size is {input_size}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BtGNbqlSKnC-"
   },
   "source": [
    "3.1.2 使用线性回归识别手写字母"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4EGSiDjsLPlD",
    "outputId": "04cdbdd5-702b-47aa-9f19-dbdecdddadff"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/10], Step [1000/1429], Loss: 4.0838\n",
      "Average error on test set: 0.2116\n",
      "Epoch [2/10], Step [1000/1429], Loss: 5.2959\n",
      "Average error on test set: 0.2167\n",
      "Epoch [3/10], Step [1000/1429], Loss: 5.5687\n",
      "Average error on test set: 0.2264\n",
      "Epoch [4/10], Step [1000/1429], Loss: 2.9196\n",
      "Average error on test set: 0.2188\n",
      "Epoch [5/10], Step [1000/1429], Loss: 5.4606\n",
      "Average error on test set: 0.2368\n",
      "Epoch [6/10], Step [1000/1429], Loss: 2.8529\n",
      "Average error on test set: 0.2185\n",
      "Epoch [7/10], Step [1000/1429], Loss: 4.3488\n",
      "Average error on test set: 0.2064\n",
      "Epoch [8/10], Step [1000/1429], Loss: 4.3501\n",
      "Average error on test set: 0.2282\n",
      "Epoch [9/10], Step [1000/1429], Loss: 4.7719\n",
      "Average error on test set: 0.2315\n",
      "Epoch [10/10], Step [1000/1429], Loss: 2.9497\n",
      "Average error on test set: 0.2376\n"
     ]
    }
   ],
   "source": [
    "# Define linear regression model\n",
    "model = nn.Linear(input_size, 1, device=device)\n",
    "\n",
    "# Define loss function\n",
    "criterion = nn.MSELoss()\n",
    "\n",
    "# Use Stochastic Gradient Descent optimizer\n",
    "optimizer = optim.Adam(model.parameters(), lr=0.03)\n",
    "\n",
    "max_epoch = 10\n",
    "for epoch in range(max_epoch):  # loop over the dataset multiple times\n",
    "    for i, (batch_image, batch_label) in enumerate(trainloader):\n",
    "        # Flatten images\n",
    "        batch_image = batch_image.view(-1, input_size)\n",
    "        batch_label = batch_label.float()\n",
    "\n",
    "        # Forward pass\n",
    "        batch_pred = model(batch_image)\n",
    "        loss = criterion(batch_pred.squeeze(), batch_label)\n",
    "\n",
    "        # Backward pass and optimization\n",
    "        optimizer.zero_grad()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        # Print propress\n",
    "        if (i+1) % 1000 == 0:\n",
    "            print(f'Epoch [{epoch+1}/{max_epoch}], Step [{i+1}/{len(trainloader)}], Loss: {loss.item():.4f}')\n",
    "\n",
    "    # Evaluation on test set\n",
    "    with torch.no_grad():\n",
    "        total_error = 0\n",
    "        for x, y in testloader:\n",
    "            y = y.squeeze().float()\n",
    "\n",
    "            x = x.view(-1, input_size)\n",
    "            y_pred = model(x).squeeze()\n",
    "\n",
    "            total_error += torch.sum((torch.abs(y_pred - y) < 0.5).float())  # Calculate mean absolute error\n",
    "\n",
    "        avg_error = total_error / len(testloader.dataset)\n",
    "        print(f\"Average error on test set: {avg_error.item():.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j2CdFQFnL_jA"
   },
   "source": [
    "3.1.3 使用逻辑回归识别手写字母\"1\"\n",
    "\n",
    "构建二分类数据集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "qdaSQw3bAouJ"
   },
   "outputs": [],
   "source": [
    "# Extract features and labels from trainset\n",
    "x_train = []\n",
    "y_train = []\n",
    "for image, label in trainset:\n",
    "    x_train.append(image.numpy())\n",
    "    y_train.append(1 if label == 1 else 0)  # Set label to 1 for character 1, 0 otherwise\n",
    "\n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "# Extract features and labels from trainset\n",
    "x_test = []\n",
    "y_test = []\n",
    "for image, label in testset:\n",
    "    x_test.append(image.numpy())\n",
    "    y_test.append(1 if label == 1 else 0)  # Set label to 1 for character 1, 0 otherwise\n",
    "\n",
    "x_test = np.array(x_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "D92HNTRc6MCC"
   },
   "source": [
    "训练逻辑回归模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "c6PY2ospzv60",
    "outputId": "b3767883-0d3c-4cbe-e186-fdd50ad9042b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 0.14 seconds\n"
     ]
    }
   ],
   "source": [
    "# Define logic regression model\n",
    "mdl_logic = LogisticRegression(max_iter=1000)\n",
    "\n",
    "# Train model\n",
    "start_time = time.time()\n",
    "mdl_logic.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oVddyjIo6P0H"
   },
   "source": [
    "模型测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Q7wjYqpI6AJM",
    "outputId": "a2e4156c-d1a6-4358-f0ad-0835d6c267a4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9665, Recall: 0.9410, Accuracy: 0.9896, F1-Score: 0.9536\n"
     ]
    }
   ],
   "source": [
    "y_pred_logic = mdl_logic.predict(x_test)\n",
    "y_proba_logic = mdl_logic.predict_proba(x_test) # Output ratio\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred_logic)\n",
    "precision = precision_score(y_test, y_pred_logic)\n",
    "recall = recall_score(y_test, y_pred_logic)\n",
    "f1 = f1_score(y_test, y_pred_logic)\n",
    "\n",
    "print(f'Precision: {precision:.4f}, Recall: {recall:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gg4PU499y9qj"
   },
   "source": [
    "案例演示：随机选取图片，输出判断结果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "JWaTWosQyt-o",
    "outputId": "1dac9a8c-4a1d-46d6-a5e3-35ec7fb4d605"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: imgDisp label is 1, x label is 1\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 2: imgDisp label is 6, x label is 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 3: imgDisp label is 2, x label is 0\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random select 3 examples from imgDisp and testset\n",
    "np.random.seed(42)\n",
    "idx = np.random.choice(len(imgDisp), 3)\n",
    "\n",
    "# Select instances\n",
    "imgDisp_select = [imgDisp[i] for i in idx]\n",
    "x_select = x_test[idx]\n",
    "y_select = y_test[idx]\n",
    "\n",
    "y_select_proba = mdl_logic.predict_proba(x_select)\n",
    "\n",
    "# Check the selected instances' labels are the same\n",
    "for i in range(len(idx)):\n",
    "    print(f'Sample {i+1}: imgDisp label is {imgDisp_select[i][1]}, x label is {y_select[i]}')\n",
    "\n",
    "    # Display image from imgDisp\n",
    "    fig, ax = plt.subplots(figsize=(7,7))\n",
    "    ax.imshow(imgDisp_select[i][0], cmap='gray')\n",
    "    ax.tick_params(axis='both', which='major', labelsize=ticklabel_size) # Set tick label size\n",
    "    ax.set_title(f\"Label: {imgDisp_select[i][1]}, Prediction: {y_select_proba[i,1]:.4f}\", fontsize=label_size)\n",
    "\n",
    "    # plt.savefig(f'binary_prediction_{i+1}.png', dpi=300) # Make figure clearer\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vK7Nd0ATTjpC"
   },
   "source": [
    "## 3.2 常用的二分类模型——支持向量机"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Pyu18U8POAqC",
    "outputId": "4675f0dc-2e39-4941-fa3f-4d39e2c4b912"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 224.64 seconds\n"
     ]
    }
   ],
   "source": [
    "# Define SVM classifier\n",
    "mdl_svm = svm.SVC(kernel='linear', probability=True)\n",
    "\n",
    "# Train model\n",
    "start_time = time.time()\n",
    "mdl_svm.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "NhRcX38R7GpP",
    "outputId": "db20fdca-acd4-4111-fb45-334f0c491b54"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9755, Recall: 0.9454, Accuracy: 0.9911, F1-Score: 0.9602\n"
     ]
    }
   ],
   "source": [
    "# Make predictions and evaluate the model\n",
    "y_pred_svm = mdl_svm.predict(x_test)\n",
    "y_proba_svm = mdl_svm.predict_proba(x_test) # Output ratio\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred_svm)\n",
    "precision = precision_score(y_test, y_pred_svm)\n",
    "recall = recall_score(y_test, y_pred_svm)\n",
    "f1 = f1_score(y_test, y_pred_svm)\n",
    "\n",
    "print(f'Precision: {precision:.4f}, Recall: {recall:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "m2njQ-yxpqju"
   },
   "source": [
    "## 3.3 常用的二分类模型——决策树和随机森林"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vEwfIl7uazAA",
    "outputId": "4abe6249-5379-41a1-8ee8-be53eb0e2cea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 5.60 seconds\n",
      "Training time: 26.31 seconds\n"
     ]
    }
   ],
   "source": [
    "# Define DecisionTree classifier\n",
    "mdl_dt = tree.DecisionTreeClassifier()\n",
    "\n",
    "# Train model\n",
    "start_time = time.time()\n",
    "mdl_dt.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')\n",
    "\n",
    "# Define Random Forest classifier\n",
    "mdl_rf = RandomForestClassifier(n_estimators=100)\n",
    "\n",
    "# Train model\n",
    "start_time = time.time()\n",
    "mdl_rf.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DiBlPKs9fNaM",
    "outputId": "70bf3572-3b2c-4888-f5cb-bee4d3aa2127"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9123, Recall: 0.9251, Accuracy: 0.9814, F1-Score: 0.9186\n",
      "Precision: 0.9897, Recall: 0.9348, Accuracy: 0.9915, F1-Score: 0.9615\n"
     ]
    }
   ],
   "source": [
    "y_pred_dt = mdl_dt.predict(x_test)\n",
    "y_proba_dt = mdl_dt.predict_proba(x_test) # Output ratio\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred_dt)\n",
    "precision = precision_score(y_test, y_pred_dt)\n",
    "recall = recall_score(y_test, y_pred_dt)\n",
    "f1 = f1_score(y_test, y_pred_dt)\n",
    "\n",
    "print(f'Precision: {precision:.4f}, Recall: {recall:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')\n",
    "\n",
    "y_pred_rf = mdl_rf.predict(x_test)\n",
    "y_proba_rf = mdl_rf.predict_proba(x_test) # Output ratio\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred_rf)\n",
    "precision = precision_score(y_test, y_pred_rf)\n",
    "recall = recall_score(y_test, y_pred_rf)\n",
    "f1 = f1_score(y_test, y_pred_rf)\n",
    "\n",
    "print(f'Precision: {precision:.4f}, Recall: {recall:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ok1YZCFqNAuH"
   },
   "source": [
    "## 3.4 二分类模型的度量"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "A9QJr7SMOCBM"
   },
   "source": [
    "准确率、召回率、敏感性、特异性、精确度、F1-Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "R1-5Gyl5SWOp",
    "outputId": "5db21c58-1745-4fee-817f-2e80615e28b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Threshold 0.5, TP: 1068, FP: 37, TN: 8828, FN: 67\n"
     ]
    }
   ],
   "source": [
    "def cls_counts(y_test, y_proba, th=0.5):\n",
    "    y_pred = (y_proba[:,1] > th).astype(int)\n",
    "\n",
    "    tp_idx = (y_test == 1) & (y_pred == 1)\n",
    "    fp_idx = (y_test == 0) & (y_pred == 1)\n",
    "    tn_idx = (y_test == 0) & (y_pred == 0)\n",
    "    fn_idx = (y_test == 1) & (y_pred == 0)\n",
    "\n",
    "    tp = np.sum(tp_idx)\n",
    "    fp = np.sum(fp_idx)\n",
    "    tn = np.sum(tn_idx)\n",
    "    fn = np.sum(fn_idx)\n",
    "\n",
    "    return th, (tp, fp, tn, fn)\n",
    "\n",
    "th, (tp, fp, tn, fn) = cls_counts(y_test, y_proba_logic)\n",
    "print(f'Threshold {th}, TP: {tp}, FP: {fp}, TN: {tn}, FN: {fn}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 522
    },
    "id": "90MR_dKrArN_",
    "outputId": "202f045a-f9ff-4e2c-c4a3-41bc015ca26b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9665, Recall (Sensitivity): 0.9410, Specificity: 0.9958, Accuracy: 0.9896, F1-Score: 0.9536\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_confusion_matrix(th, tp, fp, tn, fn):\n",
    "    \"\"\"Plots a confusion matrix given the number of true positives, false positives,\n",
    "    true negatives, and false negatives.\"\"\"\n",
    "    global label_size, ticklabel_size # Set global variables of font size\n",
    "\n",
    "    cm = np.array([[tn, fp], [fn, tp]])\n",
    "\n",
    "    # Display the confusion matrix as a heatmap\n",
    "    fig, ax = plt.subplots(figsize=(5,5))\n",
    "    img = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)\n",
    "\n",
    "    # Add labels to the axes\n",
    "    tick_marks = np.arange(2)\n",
    "    ax.set_xticks(tick_marks, ['阴(N)', '阳(P)'], fontsize=ticklabel_size)\n",
    "    ax.set_yticks(tick_marks, ['真(T)', '假(F)'], fontsize=ticklabel_size)\n",
    "\n",
    "    # Add the count of each category to the plot\n",
    "    thresh = cm.max() / 2.\n",
    "    for i in range(cm.shape[0]):\n",
    "        for j in range(cm.shape[1]):\n",
    "            plt.text(j, i, format(cm[i, j], 'd'),\n",
    "                     fontsize=ticklabel_size,\n",
    "                     horizontalalignment=\"center\",\n",
    "                     color=\"white\" if cm[i, j] > thresh else \"black\")\n",
    "\n",
    "    ax.tick_params(axis='both', which='major', labelsize=ticklabel_size) # Set tick label size\n",
    "\n",
    "    ax.set_ylabel('客观事实（Real Label）', fontsize=label_size)\n",
    "    ax.set_xlabel('主观判断（Predicted Label）', fontsize=label_size)\n",
    "    ax.set_title(f'判断阈值(Threshold): {th}', fontsize=label_size)\n",
    "\n",
    "    return fig, ax\n",
    "\n",
    "def get_scores(tp, fp, tn, fn):\n",
    "    precision = tp / (tp + fp)\n",
    "    recall = tp / (tp + fn) # Also called sensitivity\n",
    "    accuracy = (tp + tn) / (tp + fp + tn + fn)\n",
    "    f1 = 2 * precision * recall / (precision + recall)\n",
    "\n",
    "    specificity = tn / (tn + fp)\n",
    "\n",
    "    return precision, recall, specificity, accuracy, f1\n",
    "\n",
    "precision, recall, specificity, accuracy, f1 = get_scores(tp, fp, tn, fn)\n",
    "print(f'Precision: {precision:.4f}, Recall (Sensitivity): {recall:.4f}, Specificity: {specificity:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')\n",
    "\n",
    "# Example usage (replace with your actual values)\n",
    "fig, ax = plot_confusion_matrix(th, tp, fp, tn, fn)\n",
    "\n",
    "# plt.savefig(f'binary_confusion_matrix.png', dpi=300) # Make figure clearer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 522
    },
    "id": "w-rkKSZfRhdL",
    "outputId": "6a5d29d3-4469-43bd-da89-7d7e670bf61d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.7771, Recall (Sensitivity): 0.9797, Specificity: 0.9640, Accuracy: 0.9658, F1-Score: 0.8667\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "th = 0.1\n",
    "th, (tp, fp, tn, fn) = cls_counts(y_test, y_proba_logic, th)\n",
    "\n",
    "precision, recall, specificity, accuracy, f1 = get_scores(tp, fp, tn, fn)\n",
    "print(f'Precision: {precision:.4f}, Recall (Sensitivity): {recall:.4f}, Specificity: {specificity:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')\n",
    "\n",
    "fig, ax = plot_confusion_matrix(th, tp, fp, tn, fn)\n",
    "# plt.savefig(f'binary_confusion_matrix_0D1.png', dpi=300) # Make figure clearer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 522
    },
    "id": "GN6gBEqlSR7v",
    "outputId": "1526103a-97e4-4a41-f333-e2c06b8600fc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision: 0.9947, Recall (Sensitivity): 0.6643, Specificity: 0.9995, Accuracy: 0.9615, F1-Score: 0.7966\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "th = 0.9\n",
    "th, (tp, fp, tn, fn) = cls_counts(y_test, y_proba_logic, th)\n",
    "\n",
    "precision, recall, specificity, accuracy, f1 = get_scores(tp, fp, tn, fn)\n",
    "print(f'Precision: {precision:.4f}, Recall (Sensitivity): {recall:.4f}, Specificity: {specificity:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')\n",
    "\n",
    "fig, ax = plot_confusion_matrix(th, tp, fp, tn, fn)\n",
    "# plt.savefig(f'binary_confusion_matrix_0D9.png', dpi=300) # Make figure clearer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XNGhtZrLWSAY"
   },
   "source": [
    "ROC（Receiver operating characteristic curve）接收者操作特征曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 559
    },
    "id": "qCfPvjQBYJJN",
    "outputId": "5bb82ba4-6844-4a32-82e4-6ef1398d2a57"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_roc_curve_base():\n",
    "    \"\"\"Plots the ROC curve and computes AUC.\"\"\"\n",
    "    global label_size, ticklabel_size # Set global variables of font size\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(8,6))\n",
    "\n",
    "    ax.plot([0, 1], [0, 1], color='grey', lw=2, linestyle='--')\n",
    "    ax.set_xlim([0.0, 1.0])\n",
    "    ax.set_ylim([0.0, 1.0])\n",
    "    ax.tick_params(axis='both', which='major', labelsize=ticklabel_size) # Set tick label size\n",
    "\n",
    "    ax.set_xlabel('False Positive Rate (FPR)', fontsize=label_size)\n",
    "    ax.set_ylabel('True Positive Rate (TPR)', fontsize=label_size)\n",
    "\n",
    "    return fig, ax\n",
    "\n",
    "def add_roc_curve(ax, y_true, y_proba, curve_color, curve_label):\n",
    "    \"\"\"Plots the ROC curve and computes AUC.\"\"\"\n",
    "\n",
    "    fpr, tpr, thresholds = roc_curve(y_true, y_proba)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    roc = ax.plot(fpr, tpr, color=curve_color, lw=2, label=f'{curve_label} (AUC = {roc_auc:.4f})')\n",
    "\n",
    "    return roc_auc, fpr, tpr, thresholds\n",
    "\n",
    "fig, ax = plot_roc_curve_base()\n",
    "\n",
    "roc_auc_logic, fpr_logic, tpr_logic, thresholds_logic = add_roc_curve(ax, y_test, y_proba_logic[:,1], color_list[0], '逻辑回归')\n",
    "roc_auc_logic, fpr_logic, tpr_logic, thresholds_logic = add_roc_curve(ax, y_test, y_proba_svm[:,1], color_list[1], '支持向量机')\n",
    "roc_auc_logic, fpr_logic, tpr_logic, thresholds_logic = add_roc_curve(ax, y_test, y_proba_dt[:,1], color_list[2], '决策树')\n",
    "roc_auc_logic, fpr_logic, tpr_logic, thresholds_logic = add_roc_curve(ax, y_test, y_proba_rf[:,1], color_list[3], '随机森林')\n",
    "\n",
    "plt.legend(loc=\"lower right\", fontsize=ticklabel_size)\n",
    "# plt.savefig(f'binary_roc_curve.png', dpi=300) # Make figure clearer\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gapReol-qyYW"
   },
   "source": [
    "## 3.5 由二分类到多分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "IcqIRYK3m_8e"
   },
   "outputs": [],
   "source": [
    "# Extract features and labels from trainset\n",
    "x_train = []\n",
    "y_train = []\n",
    "for image, label in trainset:\n",
    "    x_train.append(image.numpy())\n",
    "    y_train.append(label)\n",
    "\n",
    "x_train = np.array(x_train)\n",
    "y_train = np.array(y_train)\n",
    "\n",
    "# Extract features and labels from trainset\n",
    "x_test = []\n",
    "y_test = []\n",
    "for image, label in testset:\n",
    "    x_test.append(image.numpy())\n",
    "    y_test.append(label)\n",
    "\n",
    "x_test = np.array(x_test)\n",
    "y_test = np.array(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Sjl2lRIOT3W"
   },
   "source": [
    "3.5.1 一对多（One-vs-Rest）方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "ZCLadsuz1cyN",
    "outputId": "9d7fca6c-d1bc-4ce6-9c7d-954f16ca54b9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 2.13 seconds\n",
      "Accuracy: 0.8535\n"
     ]
    }
   ],
   "source": [
    "# Define logic multi-classifier\n",
    "mdl_logic_ovr = OneVsRestClassifier(LogisticRegression(max_iter=1000))\n",
    "\n",
    "# Train model\n",
    "start_time = time.time()\n",
    "mdl_logic_ovr.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')\n",
    "\n",
    "# Make predictions and evaluate the model\n",
    "y_pred_logic_ovr = mdl_logic_ovr.predict(x_test)\n",
    "y_proba_logic_ovr = mdl_logic_ovr.predict_proba(x_test) # Output ratio\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred_logic_ovr)\n",
    "print(f'Accuracy: {accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JKwTr3HTO_ZG",
    "outputId": "d0ec57ad-eee1-411b-c422-225040901c63"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training class 0, Training time: 0.19 seconds\n",
      "Training class 1, Training time: 0.15 seconds\n",
      "Training class 2, Training time: 0.23 seconds\n",
      "Training class 3, Training time: 0.26 seconds\n",
      "Training class 4, Training time: 0.20 seconds\n",
      "Training class 5, Training time: 0.31 seconds\n",
      "Training class 6, Training time: 0.18 seconds\n",
      "Training class 7, Training time: 0.22 seconds\n",
      "Training class 8, Training time: 0.33 seconds\n",
      "Training class 9, Training time: 0.22 seconds\n"
     ]
    }
   ],
   "source": [
    "# Get class list: 0, 1, ..., 9\n",
    "class_list = np.sort(np.unique(y_train))\n",
    "\n",
    "# Create model list\n",
    "mdl_logic_list = []\n",
    "for c in class_list:\n",
    "    mdl_logic_list.append(LogisticRegression(max_iter=1000))\n",
    "\n",
    "# Train models seperately\n",
    "for i in range(len(class_list)):\n",
    "    start_time = time.time()\n",
    "    mdl_logic_list[i].fit(x_train, (y_train == class_list[i]).astype(int))\n",
    "    end_time = time.time()\n",
    "    print(f'Training class {class_list[i]}, Training time: {end_time - start_time:.2f} seconds')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 559
    },
    "id": "ypZ5d4NydeM1",
    "outputId": "b84e4420-fa31-4922-d51b-e13e61a571f3"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot ROC curve\n",
    "fig, ax = plot_roc_curve_base()\n",
    "\n",
    "# Draw ROC of individual classifier\n",
    "for i in range(len(class_list)):\n",
    "    # Make predictions and evaluate the model\n",
    "    y_test_trans = (y_test == class_list[i]).astype(int)\n",
    "    y_proba = mdl_logic_list[i].predict_proba(x_test) # Output ratio\n",
    "\n",
    "    roc_auc_logic, fpr_logic, tpr_logic, thresholds_logic = add_roc_curve(ax, y_test_trans, y_proba[:,1], color_list[i], f'{class_list[i]}')\n",
    "\n",
    "plt.legend(loc=\"lower right\", fontsize=ticklabel_size)\n",
    "# plt.savefig(f'binary_roc_curve_ovr.png', dpi=300) # Make figure clearer\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "vsSNVXr4-Ir-",
    "outputId": "e209bb61-0e22-4ced-b74a-db1fbec50ac6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 1: imgDisp label is 9, testset label is 9, predict label is 9\n",
      "Sample 2: imgDisp label is 2, testset label is 2, predict label is 0\n",
      "Sample 3: imgDisp label is 2, testset label is 2, predict label is 2\n",
      "Sample 4: imgDisp label is 9, testset label is 9, predict label is 8\n",
      "Sample 5: imgDisp label is 8, testset label is 8, predict label is 8\n",
      "Sample 6: imgDisp label is 5, testset label is 5, predict label is 5\n",
      "Sample 7: imgDisp label is 7, testset label is 7, predict label is 7\n",
      "Sample 8: imgDisp label is 7, testset label is 7, predict label is 7\n",
      "Sample 9: imgDisp label is 4, testset label is 4, predict label is 4\n",
      "Sample 10: imgDisp label is 5, testset label is 5, predict label is 5\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_num = 10\n",
    "\n",
    "# Random select 3 examples from imgDisp and testset\n",
    "np.random.seed(1)\n",
    "idx = np.random.choice(len(imgDisp), sample_num)\n",
    "\n",
    "# Select instances\n",
    "imgDisp_select = [imgDisp[i] for i in idx]\n",
    "testset_select = [testset[i] for i in idx]\n",
    "\n",
    "# Check the selected instances' labels are the same\n",
    "for i in range(sample_num):\n",
    "    x = testset_select[i][0].view(-1, input_size)\n",
    "\n",
    "    # Using model to predict character\n",
    "    y_pred_list = []\n",
    "    for j in range(len(mdl_logic_list)):\n",
    "        y_pred_list.append(mdl_logic_list[j].predict(x))\n",
    "\n",
    "    y_pred = np.argmax(np.array(y_pred_list), axis=0)[0]\n",
    "\n",
    "    # Display image from imgDisp\n",
    "    fig, ax = plt.subplots(figsize=(7,7))\n",
    "    ax.imshow(imgDisp_select[i][0], cmap='gray')\n",
    "    ax.tick_params(axis='both', which='major', labelsize=ticklabel_size) # Set tick label size\n",
    "    ax.set_title(f\"Label: {imgDisp_select[i][1]}, Prediction Label: {y_pred}\", fontsize=label_size)\n",
    "    # plt.savefig(f'sample_{i+1}_label_{imgDisp_select[i][1]}_pred_{y_pred}.png', dpi=300)\n",
    "    print(f'Sample {i+1}: imgDisp label is {imgDisp_select[i][1]}, testset label is {testset_select[i][1]}, predict label is {y_pred}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BZjng2_U0v1d",
    "outputId": "99c4f34a-fadc-49b1-d1ff-93c3be06014c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.7566\n"
     ]
    }
   ],
   "source": [
    "# Prediction\n",
    "y_pred_list = []\n",
    "for i in range(len(mdl_logic_list)):\n",
    "    y_pred_list.append(mdl_logic_list[i].predict(x_test))\n",
    "\n",
    "y_pred = np.argmax(np.array(y_pred_list), axis=0)\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wxvDfOE9HEsD"
   },
   "source": [
    "混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 782
    },
    "id": "PXC8Yi62HHVb",
    "outputId": "c68a32c7-3a54-492e-fb6f-83ae02ca851c"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion matrix\n",
    "cm_test = np.zeros((10, 10))\n",
    "for i in range(len(y_test)):\n",
    "    cm_test[y_test[i], y_pred[i]] += 1\n",
    "\n",
    "# Display confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(9,9))\n",
    "im = ax.imshow(cm_test, cmap=plt.cm.Blues, interpolation='nearest')\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "for i in range(cm_test.shape[0]):\n",
    "    for j in range(cm_test.shape[1]):\n",
    "        ax.text(j, i, cm_test[i, j], fontsize=ticklabel_size, ha=\"center\", va=\"center\",\n",
    "                color=\"white\" if cm_test[i, j] > cm_test.max() / 2. else \"black\")\n",
    "\n",
    "ax.set_xlabel('Predicted label', fontsize=label_size)\n",
    "ax.set_ylabel('True label', fontsize=label_size)\n",
    "\n",
    "ax.set_xticks(np.arange(10))\n",
    "ax.set_xticklabels(np.arange(10))\n",
    "\n",
    "ax.set_yticks(np.arange(10))\n",
    "ax.set_yticklabels(np.arange(10))\n",
    "\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "# plt.savefig(f'confusion_matrix_numel.png', dpi=300) # Make figure clearer\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 782
    },
    "id": "8y4cmMSMLNW0",
    "outputId": "b6cd9070-ada0-4fac-eb06-c535d8f358d2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create confusion matrix\n",
    "cm_test = np.zeros((10, 10))\n",
    "for i in range(len(y_test)):\n",
    "    cm_test[y_test[i], y_pred[i]] += 1\n",
    "\n",
    "# Change value to ratio\n",
    "cm_test = cm_test / np.sum(cm_test, axis=1, keepdims=True)\n",
    "\n",
    "# Display confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(9,9))\n",
    "im = ax.imshow(cm_test, cmap=plt.cm.Blues, interpolation='nearest')\n",
    "\n",
    "# Loop over data dimensions and create text annotations.\n",
    "for i in range(cm_test.shape[0]):\n",
    "    for j in range(cm_test.shape[1]):\n",
    "        ax.text(j, i, format(cm_test[i, j], '.2f'), fontsize=ticklabel_size, ha=\"center\", va=\"center\",\n",
    "                color=\"white\" if cm_test[i, j] > cm_test.max() / 2. else \"black\")\n",
    "\n",
    "ax.set_xlabel('Predicted label', fontsize=label_size)\n",
    "ax.set_ylabel('True label', fontsize=label_size)\n",
    "\n",
    "ax.set_xticks(np.arange(10))\n",
    "ax.set_xticklabels(np.arange(10))\n",
    "\n",
    "ax.set_yticks(np.arange(10))\n",
    "ax.set_yticklabels(np.arange(10))\n",
    "\n",
    "ax.tick_params(axis='both', which='major', labelsize=ticklabel_size)\n",
    "\n",
    "# plt.savefig(f'confusion_matrix_ratio.png', dpi=300) # Make figure clearer\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "oRXvzocEbGWj"
   },
   "source": [
    "3.5.2 一对一（One-vs-One）方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "XP8-K-A7pwq_",
    "outputId": "912700a3-b4b8-40a1-f3c3-05424001cded"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 1.24 seconds\n",
      "Accuracy: 0.8778\n"
     ]
    }
   ],
   "source": [
    "# Define logic regression classifier\n",
    "mdl_logic_ovo = OneVsOneClassifier(LogisticRegression(max_iter=1000))\n",
    "\n",
    "# Train model\n",
    "start_time = time.time()\n",
    "mdl_logic_ovo.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')\n",
    "\n",
    "# Make predictions and evaluate the model\n",
    "y_pred = mdl_logic_ovo.predict(x_test)\n",
    "\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e69VMFy-lkg-",
    "outputId": "bd33372e-0617-4da7-e00d-ea7de45d3ad4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training class 0 (5923) vs class 1 (6742), Training time: 0.02 seconds, Precision: 0.9908, Recall (Sensitivity): 0.9929, Specificity: 0.9921, Accuracy: 0.9924, F1-Score: 0.9918\n",
      "Training class 0 (5923) vs class 2 (5958), Training time: 0.02 seconds, Precision: 0.9819, Recall (Sensitivity): 0.9939, Specificity: 0.9826, Accuracy: 0.9881, F1-Score: 0.9878\n",
      "Training class 0 (5923) vs class 3 (6131), Training time: 0.02 seconds, Precision: 0.9929, Recall (Sensitivity): 0.9939, Specificity: 0.9931, Accuracy: 0.9935, F1-Score: 0.9934\n",
      "Training class 0 (5923) vs class 4 (5842), Training time: 0.02 seconds, Precision: 0.9959, Recall (Sensitivity): 0.9898, Specificity: 0.9959, Accuracy: 0.9929, F1-Score: 0.9928\n",
      "Training class 0 (5923) vs class 5 (5421), Training time: 0.02 seconds, Precision: 0.9698, Recall (Sensitivity): 0.9837, Specificity: 0.9664, Accuracy: 0.9754, F1-Score: 0.9767\n",
      "Training class 0 (5923) vs class 6 (5918), Training time: 0.02 seconds, Precision: 0.9828, Recall (Sensitivity): 0.9888, Specificity: 0.9823, Accuracy: 0.9856, F1-Score: 0.9858\n",
      "Training class 0 (5923) vs class 7 (6265), Training time: 0.02 seconds, Precision: 0.9838, Recall (Sensitivity): 0.9929, Specificity: 0.9844, Accuracy: 0.9885, F1-Score: 0.9883\n",
      "Training class 0 (5923) vs class 8 (5851), Training time: 0.04 seconds, Precision: 0.9776, Recall (Sensitivity): 0.9776, Specificity: 0.9774, Accuracy: 0.9775, F1-Score: 0.9776\n",
      "Training class 0 (5923) vs class 9 (5949), Training time: 0.02 seconds, Precision: 0.9788, Recall (Sensitivity): 0.9908, Specificity: 0.9792, Accuracy: 0.9849, F1-Score: 0.9848\n",
      "Training class 1 (6742) vs class 0 (5923), Training time: 0.02 seconds, Precision: 0.9938, Recall (Sensitivity): 0.9921, Specificity: 0.9929, Accuracy: 0.9924, F1-Score: 0.9929\n",
      "Training class 1 (6742) vs class 2 (5958), Training time: 0.03 seconds, Precision: 0.9851, Recall (Sensitivity): 0.9930, Specificity: 0.9835, Accuracy: 0.9885, F1-Score: 0.9890\n",
      "Training class 1 (6742) vs class 3 (6131), Training time: 0.02 seconds, Precision: 0.9672, Recall (Sensitivity): 0.9885, Specificity: 0.9624, Accuracy: 0.9762, F1-Score: 0.9778\n",
      "Training class 1 (6742) vs class 4 (5842), Training time: 0.02 seconds, Precision: 0.9974, Recall (Sensitivity): 0.9956, Specificity: 0.9969, Accuracy: 0.9962, F1-Score: 0.9965\n",
      "Training class 1 (6742) vs class 5 (5421), Training time: 0.03 seconds, Precision: 0.9809, Recall (Sensitivity): 0.9938, Specificity: 0.9753, Accuracy: 0.9857, F1-Score: 0.9873\n",
      "Training class 1 (6742) vs class 6 (5918), Training time: 0.02 seconds, Precision: 0.9834, Recall (Sensitivity): 0.9930, Specificity: 0.9802, Accuracy: 0.9871, F1-Score: 0.9882\n",
      "Training class 1 (6742) vs class 7 (6265), Training time: 0.02 seconds, Precision: 0.9742, Recall (Sensitivity): 0.9991, Specificity: 0.9708, Accuracy: 0.9857, F1-Score: 0.9865\n",
      "Training class 1 (6742) vs class 8 (5851), Training time: 0.02 seconds, Precision: 0.9735, Recall (Sensitivity): 0.9700, Specificity: 0.9692, Accuracy: 0.9697, F1-Score: 0.9718\n",
      "Training class 1 (6742) vs class 9 (5949), Training time: 0.02 seconds, Precision: 0.9844, Recall (Sensitivity): 1.0000, Specificity: 0.9822, Accuracy: 0.9916, F1-Score: 0.9921\n",
      "Training class 2 (5958) vs class 0 (5923), Training time: 0.02 seconds, Precision: 0.9941, Recall (Sensitivity): 0.9826, Specificity: 0.9939, Accuracy: 0.9881, F1-Score: 0.9883\n",
      "Training class 2 (5958) vs class 1 (6742), Training time: 0.02 seconds, Precision: 0.9922, Recall (Sensitivity): 0.9835, Specificity: 0.9930, Accuracy: 0.9885, F1-Score: 0.9878\n",
      "Training class 2 (5958) vs class 3 (6131), Training time: 0.02 seconds, Precision: 0.9519, Recall (Sensitivity): 0.9390, Specificity: 0.9515, Accuracy: 0.9452, F1-Score: 0.9454\n",
      "Training class 2 (5958) vs class 4 (5842), Training time: 0.02 seconds, Precision: 0.9941, Recall (Sensitivity): 0.9855, Specificity: 0.9939, Accuracy: 0.9896, F1-Score: 0.9898\n",
      "Training class 2 (5958) vs class 5 (5421), Training time: 0.02 seconds, Precision: 0.9254, Recall (Sensitivity): 0.9138, Specificity: 0.9148, Accuracy: 0.9142, F1-Score: 0.9196\n",
      "Training class 2 (5958) vs class 6 (5918), Training time: 0.05 seconds, Precision: 0.9614, Recall (Sensitivity): 0.9651, Specificity: 0.9582, Accuracy: 0.9618, F1-Score: 0.9632\n",
      "Training class 2 (5958) vs class 7 (6265), Training time: 0.02 seconds, Precision: 0.9779, Recall (Sensitivity): 0.9845, Specificity: 0.9776, Accuracy: 0.9811, F1-Score: 0.9812\n",
      "Training class 2 (5958) vs class 8 (5851), Training time: 0.03 seconds, Precision: 0.9900, Recall (Sensitivity): 0.9583, Specificity: 0.9897, Accuracy: 0.9736, F1-Score: 0.9739\n",
      "Training class 2 (5958) vs class 9 (5949), Training time: 0.02 seconds, Precision: 0.9836, Recall (Sensitivity): 0.9893, Specificity: 0.9832, Accuracy: 0.9863, F1-Score: 0.9865\n",
      "Training class 3 (6131) vs class 0 (5923), Training time: 0.02 seconds, Precision: 0.9941, Recall (Sensitivity): 0.9931, Specificity: 0.9939, Accuracy: 0.9935, F1-Score: 0.9936\n",
      "Training class 3 (6131) vs class 1 (6742), Training time: 0.02 seconds, Precision: 0.9868, Recall (Sensitivity): 0.9624, Specificity: 0.9885, Accuracy: 0.9762, F1-Score: 0.9744\n",
      "Training class 3 (6131) vs class 2 (5958), Training time: 0.02 seconds, Precision: 0.9385, Recall (Sensitivity): 0.9515, Specificity: 0.9390, Accuracy: 0.9452, F1-Score: 0.9449\n",
      "Training class 3 (6131) vs class 4 (5842), Training time: 0.02 seconds, Precision: 1.0000, Recall (Sensitivity): 0.9891, Specificity: 1.0000, Accuracy: 0.9945, F1-Score: 0.9945\n",
      "Training class 3 (6131) vs class 5 (5421), Training time: 0.03 seconds, Precision: 0.8746, Recall (Sensitivity): 0.9257, Specificity: 0.8498, Accuracy: 0.8901, F1-Score: 0.8995\n",
      "Training class 3 (6131) vs class 6 (5918), Training time: 0.02 seconds, Precision: 0.9901, Recall (Sensitivity): 0.9911, Specificity: 0.9896, Accuracy: 0.9903, F1-Score: 0.9906\n",
      "Training class 3 (6131) vs class 7 (6265), Training time: 0.02 seconds, Precision: 0.9660, Recall (Sensitivity): 0.9564, Specificity: 0.9669, Accuracy: 0.9617, F1-Score: 0.9612\n",
      "Training class 3 (6131) vs class 8 (5851), Training time: 0.05 seconds, Precision: 0.9592, Recall (Sensitivity): 0.9554, Specificity: 0.9579, Accuracy: 0.9567, F1-Score: 0.9573\n",
      "Training class 3 (6131) vs class 9 (5949), Training time: 0.02 seconds, Precision: 0.9714, Recall (Sensitivity): 0.9743, Specificity: 0.9713, Accuracy: 0.9728, F1-Score: 0.9728\n",
      "Training class 4 (5842) vs class 0 (5923), Training time: 0.02 seconds, Precision: 0.9899, Recall (Sensitivity): 0.9959, Specificity: 0.9898, Accuracy: 0.9929, F1-Score: 0.9929\n",
      "Training class 4 (5842) vs class 1 (6742), Training time: 0.03 seconds, Precision: 0.9949, Recall (Sensitivity): 0.9969, Specificity: 0.9956, Accuracy: 0.9962, F1-Score: 0.9959\n",
      "Training class 4 (5842) vs class 2 (5958), Training time: 0.02 seconds, Precision: 0.9849, Recall (Sensitivity): 0.9939, Specificity: 0.9855, Accuracy: 0.9896, F1-Score: 0.9894\n",
      "Training class 4 (5842) vs class 3 (6131), Training time: 0.02 seconds, Precision: 0.9889, Recall (Sensitivity): 1.0000, Specificity: 0.9891, Accuracy: 0.9945, F1-Score: 0.9944\n",
      "Training class 4 (5842) vs class 5 (5421), Training time: 0.02 seconds, Precision: 0.9799, Recall (Sensitivity): 0.9929, Specificity: 0.9776, Accuracy: 0.9856, F1-Score: 0.9863\n",
      "Training class 4 (5842) vs class 6 (5918), Training time: 0.02 seconds, Precision: 0.9858, Recall (Sensitivity): 0.9919, Specificity: 0.9854, Accuracy: 0.9887, F1-Score: 0.9888\n",
      "Training class 4 (5842) vs class 7 (6265), Training time: 0.03 seconds, Precision: 0.9770, Recall (Sensitivity): 0.9939, Specificity: 0.9776, Accuracy: 0.9856, F1-Score: 0.9854\n",
      "Training class 4 (5842) vs class 8 (5851), Training time: 0.03 seconds, Precision: 0.9859, Recall (Sensitivity): 0.9949, Specificity: 0.9856, Accuracy: 0.9903, F1-Score: 0.9904\n",
      "Training class 4 (5842) vs class 9 (5949), Training time: 0.03 seconds, Precision: 0.9407, Recall (Sensitivity): 0.9532, Specificity: 0.9415, Accuracy: 0.9473, F1-Score: 0.9469\n",
      "Training class 5 (5421) vs class 0 (5923), Training time: 0.02 seconds, Precision: 0.9818, Recall (Sensitivity): 0.9664, Specificity: 0.9837, Accuracy: 0.9754, F1-Score: 0.9740\n",
      "Training class 5 (5421) vs class 1 (6742), Training time: 0.03 seconds, Precision: 0.9920, Recall (Sensitivity): 0.9753, Specificity: 0.9938, Accuracy: 0.9857, F1-Score: 0.9836\n",
      "Training class 5 (5421) vs class 2 (5958), Training time: 0.02 seconds, Precision: 0.9017, Recall (Sensitivity): 0.9148, Specificity: 0.9138, Accuracy: 0.9142, F1-Score: 0.9082\n",
      "Training class 5 (5421) vs class 3 (6131), Training time: 0.03 seconds, Precision: 0.9100, Recall (Sensitivity): 0.8498, Specificity: 0.9257, Accuracy: 0.8901, F1-Score: 0.8788\n",
      "Training class 5 (5421) vs class 4 (5842), Training time: 0.01 seconds, Precision: 0.9920, Recall (Sensitivity): 0.9776, Specificity: 0.9929, Accuracy: 0.9856, F1-Score: 0.9848\n",
      "Training class 5 (5421) vs class 6 (5918), Training time: 0.02 seconds, Precision: 0.9787, Recall (Sensitivity): 0.9809, Specificity: 0.9802, Accuracy: 0.9805, F1-Score: 0.9798\n",
      "Training class 5 (5421) vs class 7 (6265), Training time: 0.03 seconds, Precision: 0.9607, Recall (Sensitivity): 0.9585, Specificity: 0.9660, Accuracy: 0.9625, F1-Score: 0.9596\n",
      "Training class 5 (5421) vs class 8 (5851), Training time: 0.03 seconds, Precision: 0.9448, Recall (Sensitivity): 0.9025, Specificity: 0.9517, Accuracy: 0.9282, F1-Score: 0.9232\n",
      "Training class 5 (5421) vs class 9 (5949), Training time: 0.02 seconds, Precision: 0.9644, Recall (Sensitivity): 0.9731, Specificity: 0.9683, Accuracy: 0.9705, F1-Score: 0.9687\n",
      "Training class 6 (5918) vs class 0 (5923), Training time: 0.02 seconds, Precision: 0.9884, Recall (Sensitivity): 0.9823, Specificity: 0.9888, Accuracy: 0.9856, F1-Score: 0.9853\n",
      "Training class 6 (5918) vs class 1 (6742), Training time: 0.01 seconds, Precision: 0.9916, Recall (Sensitivity): 0.9802, Specificity: 0.9930, Accuracy: 0.9871, F1-Score: 0.9858\n",
      "Training class 6 (5918) vs class 2 (5958), Training time: 0.04 seconds, Precision: 0.9623, Recall (Sensitivity): 0.9582, Specificity: 0.9651, Accuracy: 0.9618, F1-Score: 0.9603\n",
      "Training class 6 (5918) vs class 3 (6131), Training time: 0.02 seconds, Precision: 0.9906, Recall (Sensitivity): 0.9896, Specificity: 0.9911, Accuracy: 0.9903, F1-Score: 0.9901\n",
      "Training class 6 (5918) vs class 4 (5842), Training time: 0.01 seconds, Precision: 0.9916, Recall (Sensitivity): 0.9854, Specificity: 0.9919, Accuracy: 0.9887, F1-Score: 0.9885\n",
      "Training class 6 (5918) vs class 5 (5421), Training time: 0.01 seconds, Precision: 0.9822, Recall (Sensitivity): 0.9802, Specificity: 0.9809, Accuracy: 0.9805, F1-Score: 0.9812\n",
      "Training class 6 (5918) vs class 7 (6265), Training time: 0.02 seconds, Precision: 0.9969, Recall (Sensitivity): 0.9969, Specificity: 0.9971, Accuracy: 0.9970, F1-Score: 0.9969\n",
      "Training class 6 (5918) vs class 8 (5851), Training time: 0.02 seconds, Precision: 0.9790, Recall (Sensitivity): 0.9749, Specificity: 0.9795, Accuracy: 0.9772, F1-Score: 0.9770\n",
      "Training class 6 (5918) vs class 9 (5949), Training time: 0.01 seconds, Precision: 0.9948, Recall (Sensitivity): 0.9969, Specificity: 0.9950, Accuracy: 0.9959, F1-Score: 0.9958\n",
      "Training class 7 (6265) vs class 0 (5923), Training time: 0.01 seconds, Precision: 0.9931, Recall (Sensitivity): 0.9844, Specificity: 0.9929, Accuracy: 0.9885, F1-Score: 0.9888\n",
      "Training class 7 (6265) vs class 1 (6742), Training time: 0.02 seconds, Precision: 0.9990, Recall (Sensitivity): 0.9708, Specificity: 0.9991, Accuracy: 0.9857, F1-Score: 0.9847\n",
      "Training class 7 (6265) vs class 2 (5958), Training time: 0.02 seconds, Precision: 0.9843, Recall (Sensitivity): 0.9776, Specificity: 0.9845, Accuracy: 0.9811, F1-Score: 0.9810\n",
      "Training class 7 (6265) vs class 3 (6131), Training time: 0.02 seconds, Precision: 0.9576, Recall (Sensitivity): 0.9669, Specificity: 0.9564, Accuracy: 0.9617, F1-Score: 0.9622\n",
      "Training class 7 (6265) vs class 4 (5842), Training time: 0.02 seconds, Precision: 0.9941, Recall (Sensitivity): 0.9776, Specificity: 0.9939, Accuracy: 0.9856, F1-Score: 0.9858\n",
      "Training class 7 (6265) vs class 5 (5421), Training time: 0.03 seconds, Precision: 0.9641, Recall (Sensitivity): 0.9660, Specificity: 0.9585, Accuracy: 0.9625, F1-Score: 0.9650\n",
      "Training class 7 (6265) vs class 6 (5918), Training time: 0.01 seconds, Precision: 0.9971, Recall (Sensitivity): 0.9971, Specificity: 0.9969, Accuracy: 0.9970, F1-Score: 0.9971\n",
      "Training class 7 (6265) vs class 8 (5851), Training time: 0.02 seconds, Precision: 0.9688, Recall (Sensitivity): 0.9669, Specificity: 0.9671, Accuracy: 0.9670, F1-Score: 0.9679\n",
      "Training class 7 (6265) vs class 9 (5949), Training time: 0.04 seconds, Precision: 0.9676, Recall (Sensitivity): 0.9309, Specificity: 0.9683, Accuracy: 0.9494, F1-Score: 0.9489\n",
      "Training class 8 (5851) vs class 0 (5923), Training time: 0.03 seconds, Precision: 0.9774, Recall (Sensitivity): 0.9774, Specificity: 0.9776, Accuracy: 0.9775, F1-Score: 0.9774\n",
      "Training class 8 (5851) vs class 1 (6742), Training time: 0.02 seconds, Precision: 0.9652, Recall (Sensitivity): 0.9692, Specificity: 0.9700, Accuracy: 0.9697, F1-Score: 0.9672\n",
      "Training class 8 (5851) vs class 2 (5958), Training time: 0.02 seconds, Precision: 0.9573, Recall (Sensitivity): 0.9897, Specificity: 0.9583, Accuracy: 0.9736, F1-Score: 0.9732\n",
      "Training class 8 (5851) vs class 3 (6131), Training time: 0.04 seconds, Precision: 0.9540, Recall (Sensitivity): 0.9579, Specificity: 0.9554, Accuracy: 0.9567, F1-Score: 0.9559\n",
      "Training class 8 (5851) vs class 4 (5842), Training time: 0.03 seconds, Precision: 0.9948, Recall (Sensitivity): 0.9856, Specificity: 0.9949, Accuracy: 0.9903, F1-Score: 0.9902\n",
      "Training class 8 (5851) vs class 5 (5421), Training time: 0.03 seconds, Precision: 0.9142, Recall (Sensitivity): 0.9517, Specificity: 0.9025, Accuracy: 0.9282, F1-Score: 0.9326\n",
      "Training class 8 (5851) vs class 6 (5918), Training time: 0.02 seconds, Precision: 0.9755, Recall (Sensitivity): 0.9795, Specificity: 0.9749, Accuracy: 0.9772, F1-Score: 0.9775\n",
      "Training class 8 (5851) vs class 7 (6265), Training time: 0.03 seconds, Precision: 0.9652, Recall (Sensitivity): 0.9671, Specificity: 0.9669, Accuracy: 0.9670, F1-Score: 0.9662\n",
      "Training class 8 (5851) vs class 9 (5949), Training time: 0.02 seconds, Precision: 0.9613, Recall (Sensitivity): 0.9702, Specificity: 0.9623, Accuracy: 0.9662, F1-Score: 0.9658\n",
      "Training class 9 (5949) vs class 0 (5923), Training time: 0.02 seconds, Precision: 0.9910, Recall (Sensitivity): 0.9792, Specificity: 0.9908, Accuracy: 0.9849, F1-Score: 0.9850\n",
      "Training class 9 (5949) vs class 1 (6742), Training time: 0.02 seconds, Precision: 1.0000, Recall (Sensitivity): 0.9822, Specificity: 1.0000, Accuracy: 0.9916, F1-Score: 0.9910\n",
      "Training class 9 (5949) vs class 2 (5958), Training time: 0.02 seconds, Precision: 0.9890, Recall (Sensitivity): 0.9832, Specificity: 0.9893, Accuracy: 0.9863, F1-Score: 0.9861\n",
      "Training class 9 (5949) vs class 3 (6131), Training time: 0.02 seconds, Precision: 0.9742, Recall (Sensitivity): 0.9713, Specificity: 0.9743, Accuracy: 0.9728, F1-Score: 0.9727\n",
      "Training class 9 (5949) vs class 4 (5842), Training time: 0.03 seconds, Precision: 0.9538, Recall (Sensitivity): 0.9415, Specificity: 0.9532, Accuracy: 0.9473, F1-Score: 0.9476\n",
      "Training class 9 (5949) vs class 5 (5421), Training time: 0.02 seconds, Precision: 0.9760, Recall (Sensitivity): 0.9683, Specificity: 0.9731, Accuracy: 0.9705, F1-Score: 0.9721\n",
      "Training class 9 (5949) vs class 6 (5918), Training time: 0.01 seconds, Precision: 0.9970, Recall (Sensitivity): 0.9950, Specificity: 0.9969, Accuracy: 0.9959, F1-Score: 0.9960\n",
      "Training class 9 (5949) vs class 7 (6265), Training time: 0.04 seconds, Precision: 0.9323, Recall (Sensitivity): 0.9683, Specificity: 0.9309, Accuracy: 0.9494, F1-Score: 0.9499\n",
      "Training class 9 (5949) vs class 8 (5851), Training time: 0.02 seconds, Precision: 0.9710, Recall (Sensitivity): 0.9623, Specificity: 0.9702, Accuracy: 0.9662, F1-Score: 0.9667\n"
     ]
    }
   ],
   "source": [
    "# Get class list: 0, 1, ..., 9\n",
    "class_list = np.sort(np.unique(y_train))\n",
    "\n",
    "# Create model matrix to save models\n",
    "mdl_logic_matrix = {}\n",
    "for cls_p in class_list:\n",
    "    mdl_logic_matrix[cls_p] = {}\n",
    "    for cls_n in class_list:\n",
    "        if cls_p == cls_n:\n",
    "            continue\n",
    "        mdl_logic_matrix[cls_p][cls_n] = LogisticRegression(max_iter=1000)\n",
    "\n",
    "for cls_p in class_list:\n",
    "    # Training data of positive class\n",
    "    x_train_ovo_p = x_train[(y_train == cls_p), :]\n",
    "    y_train_ovo_p = np.ones(x_train_ovo_p.shape[0])\n",
    "\n",
    "    # Testing data of positive class\n",
    "    x_test_ovo_p = x_test[(y_test == cls_p), :]\n",
    "    y_test_ovo_p = np.ones(x_test_ovo_p.shape[0])\n",
    "\n",
    "    for cls_n in class_list:\n",
    "        if cls_p == cls_n:\n",
    "            continue\n",
    "\n",
    "        # Training data of negative class\n",
    "        x_train_ovo_n = x_train[(y_train == cls_n), :]\n",
    "        y_train_ovo_n = np.zeros(x_train_ovo_n.shape[0])\n",
    "\n",
    "        # Testing data of negative class\n",
    "        x_test_ovo_n = x_test[(y_test == cls_n), :]\n",
    "        y_test_ovo_n = np.zeros(x_test_ovo_n.shape[0])\n",
    "\n",
    "        # Concatenate data for training\n",
    "        x_train_ovo = np.concatenate((x_train_ovo_p, x_train_ovo_n), axis=0)\n",
    "        y_train_ovo = np.concatenate((y_train_ovo_p, y_train_ovo_n), axis=0)\n",
    "\n",
    "        # Model training\n",
    "        start_time = time.time()\n",
    "        mdl_logic_matrix[cls_p][cls_n].fit(x_train_ovo, y_train_ovo)\n",
    "        end_time = time.time()\n",
    "\n",
    "        # Concatenate data for testing\n",
    "        x_test_ovo = np.concatenate((x_test_ovo_p, x_test_ovo_n), axis=0)\n",
    "        y_test_ovo = np.concatenate((y_test_ovo_p, y_test_ovo_n), axis=0)\n",
    "\n",
    "        # Test model on sub-task\n",
    "        y_proba_ovo = mdl_logic_matrix[cls_p][cls_n].predict_proba(x_test_ovo) # Output ratio\n",
    "\n",
    "        # Display results\n",
    "        _, (tp, fp, tn, fn) = cls_counts(y_test_ovo, y_proba_ovo)\n",
    "        precision, recall, specificity, accuracy, f1 = get_scores(tp, fp, tn, fn)\n",
    "        print(f'Training class {cls_p} ({x_train_ovo_p.shape[0]}) vs class {cls_n} ({x_train_ovo_n.shape[0]}), Training time: {end_time - start_time:.2f} seconds, Precision: {precision:.4f}, Recall (Sensitivity): {recall:.4f}, Specificity: {specificity:.4f}, Accuracy: {accuracy:.4f}, F1-Score: {f1:.4f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "sat6M2RLNBxB",
    "outputId": "7948d284-554e-46fe-98f4-e3bf90687bb5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8783\n"
     ]
    }
   ],
   "source": [
    "# Select class 1\n",
    "x_test_select = x_test[:, :]\n",
    "\n",
    "# Prediction\n",
    "y_pred_counts = np.zeros((x_test_select.shape[0], len(class_list)))\n",
    "\n",
    "for cls_p in class_list:\n",
    "    for cls_n in class_list:\n",
    "        if cls_p == cls_n:\n",
    "            continue\n",
    "\n",
    "        y_pred_counts[:, cls_p] = y_pred_counts[:, cls_p] + mdl_logic_matrix[cls_p][cls_n].predict(x_test_select)\n",
    "\n",
    "y_pred = np.argmax(y_pred_counts, axis=1)\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(f'Accuracy: {accuracy:.4f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RH0TMNsAoMjT"
   },
   "source": [
    "3.5.3 Softmax回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "-GlO3qSgtxgg",
    "outputId": "0776a013-a04f-4e88-c7e3-de0133f0d452"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training time: 3.11 seconds\n",
      "Accuracy: 0.8739\n"
     ]
    }
   ],
   "source": [
    "mdl_softmax = LogisticRegression(max_iter=1000, solver='lbfgs')\n",
    "\n",
    "start_time = time.time()\n",
    "mdl_softmax.fit(x_train, y_train)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')\n",
    "\n",
    "# Evaluate accuracy (or other metrics)\n",
    "y_pred = mdl_softmax.predict(x_test)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LMvKrVWqpqJL",
    "outputId": "cdcb18c0-be0a-4d5d-c94a-490954f1f67d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sample 32026,\t Class 3: [0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
      "Sample 58547,\t Class 0: [1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
      "Sample 54549,\t Class 9: [0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
      "Sample 21759,\t Class 7: [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      "Sample 45414,\t Class 8: [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
      "Sample 35731,\t Class 8: [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
      "Sample 51669,\t Class 8: [0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
      "Sample 8445,\t Class 7: [0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
      "Sample 19947,\t Class 6: [0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
      "Sample 37533,\t Class 2: [0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "# One-hot encoding\n",
    "def one_hot_encode(y, num_classes):\n",
    "    \"\"\"Converts integer labels to one-hot encoding.\"\"\"\n",
    "    one_hot = np.zeros((y.shape[0], num_classes))\n",
    "    one_hot[np.arange(y.shape[0]), y] = 1\n",
    "    return one_hot\n",
    "\n",
    "# Example usage:\n",
    "num_classes = len(class_list)\n",
    "y_train_onehot = one_hot_encode(y_train, num_classes)\n",
    "\n",
    "# Display one-hot encoding results of ten random sample\n",
    "for _ in range(10):\n",
    "    idx = np.random.randint(0, y_train_onehot.shape[0])\n",
    "\n",
    "    print(f'Sample {idx+1},\\t Class {y_train[idx]}: {y_train_onehot[idx,:]}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "id": "5brE3L-UsCYa"
   },
   "outputs": [],
   "source": [
    "# Softmax function\n",
    "def softmax(x):\n",
    "    \"\"\"Compute softmax values for each sets of scores in x.\"\"\"\n",
    "    e_x = np.exp(x - np.max(x, axis=1, keepdims=True))\n",
    "    return e_x / e_x.sum(axis=1, keepdims=True)\n",
    "\n",
    "# Cross-entropy loss\n",
    "def cross_entropy_loss(y, y_pred):\n",
    "    \"\"\"Compute cross-entropy loss.\"\"\"\n",
    "    epsilon = 1e-15  # Small value to avoid log(0)\n",
    "    loss = -np.sum(y * np.log(y_pred + epsilon)) / y.shape[0]\n",
    "    return loss\n",
    "\n",
    "def gradient_descent(x, y, learning_rate, num_iterations):\n",
    "    \"\"\"Performs gradient descent optimization.\"\"\"\n",
    "    num_samples, num_features = x.shape\n",
    "    num_classes = y.shape[1]\n",
    "\n",
    "    # Initialize weights and bias\n",
    "    w = np.random.randn(num_features, num_classes)\n",
    "    b = np.zeros(num_classes)\n",
    "\n",
    "    for i in range(num_iterations):\n",
    "        # Forward pass\n",
    "        scores = np.dot(x, w) + b\n",
    "        y_pred = softmax(scores)\n",
    "\n",
    "        # Compute loss\n",
    "        loss = cross_entropy_loss(y, y_pred)\n",
    "\n",
    "        # Backward pass (compute gradients), penalty 'l2'\n",
    "        dw = (1 / num_samples) * np.dot(x.T, (y_pred - y)) + 0.1 * w\n",
    "        db = (1 / num_samples) * np.sum(y_pred - y, axis=0)\n",
    "\n",
    "        # Update parameters\n",
    "        w -= learning_rate * dw\n",
    "        b -= learning_rate * db\n",
    "\n",
    "        if i % 100 == 0:\n",
    "            print(f'Iteration {i}, Loss: {loss}')\n",
    "\n",
    "    return w, b\n",
    "\n",
    "def predict(x, w, b):\n",
    "    \"\"\"Predicts class labels for input data.\"\"\"\n",
    "    scores = np.dot(x, w) + b\n",
    "    y_pred = softmax(scores)\n",
    "    return np.argmax(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "QiU6_YBPscjL",
    "outputId": "aafc1478-c6a8-4b31-f340-d0c4264c65d4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0, Loss: 5.870746553998726\n",
      "Iteration 100, Loss: 2.1601264242701212\n",
      "Iteration 200, Loss: 2.07857085616024\n",
      "Iteration 300, Loss: 2.057288298796678\n",
      "Iteration 400, Loss: 2.049243018452023\n",
      "Iteration 500, Loss: 2.045049907029103\n",
      "Iteration 600, Loss: 2.0423148027363665\n",
      "Iteration 700, Loss: 2.040288716593428\n",
      "Iteration 800, Loss: 2.0386918463822257\n",
      "Iteration 900, Loss: 2.037396930107844\n",
      "Training time: 49.33 seconds\n",
      "Accuracy: 0.554\n"
     ]
    }
   ],
   "source": [
    "# Perform gradient descent\n",
    "start_time = time.time()\n",
    "w, b = gradient_descent(x_train, y_train_onehot, learning_rate=0.1, num_iterations=1000)\n",
    "end_time = time.time()\n",
    "\n",
    "print(f'Training time: {end_time - start_time:.2f} seconds')\n",
    "\n",
    "# Make predictions\n",
    "y_pred = predict(x_test, w, b)\n",
    "\n",
    "# Evaluate accuracy (or other metrics)\n",
    "accuracy = accuracy_score(y_test, y_pred)\n",
    "print(\"Accuracy:\", accuracy)"
   ]
  }
 ],
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